定量光声层析成像中的高对比度高分辨率重建算法
A high contrast and resolution reconstruction algorithm in quantitative photoacoustic tomography
AI总结：
本研究针对定量光声层析成像的光学系数重建问题，提出结合稀疏正则化与Kubelka-Munk先验的Tikhonov型泛函框架，采用基于Pontryagin极大值原理的序列二次哈密顿算法求解，实现了多类物体的高对比度高分辨率重建。
摘要：
本文提出了一种用于定量光声层析成像中光学扩散系数与吸收系数重建的框架。该框架基于Tikhonov型泛函，包含促进吸收系数稀疏性的正则化项，以及引入Kubelka-Munk吸收-扩散关系的先验，可实现更优的重建效果。重建问题被建模为该泛函的最小化问题，约束条件为光子传播模型给出的微分约束。通过基于Pontryagin极大值原理的快速鲁棒序列二次哈密顿算法求解该问题。多项数值实验结果表明，所提出的计算策略能够针对多种物体实现高对比度、高分辨率的光学系数重建。
英文摘要：
A framework for reconstruction of optical diffusion and absorption coefficients in quantitative photoacoustic tomography is presented. This framework is based on a Tikhonov-type functional with a regularization term promoting sparsity of the absorption coefficient and a prior involving a Kubelka-Munk absorption-diffusion relation that allows to obtain superior reconstructions. The reconstruction problem is formulated as the minimization of this functional subject to the differential constraint given by a photon-propagation model. The solution of this problem is obtained by a fast and robust sequential quadratic hamiltonian algorithm based on the Pontryagin maximum principle. Results of several numerical experiments demonstrate that the proposed computational strategy is able to obtain reconstructions of the optical coefficients with high contrast and resolution for a wide variety of objects.
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